QRA Model‐Based Risk Impact Analysis of Traffic Flow in Urban Road Tunnels

Road tunnels are vital infrastructures providing underground vehicular passageways for commuters and motorists. Various quantitative risk assessment (QRA) models have recently been developed and employed to evaluate the safety levels of road tunnels in terms of societal risk (as measured by the F/N curve). For a particular road tunnel, traffic volume and proportion of heavy goods vehicles (HGVs) are two adjustable parameters that may significantly affect the societal risk, and are thus very useful in implementing risk reduction solutions. To evaluate the impact the two contributing factors have on the risk, this article first presents an approach that employs a QRA model to generate societal risk for a series of possible combinations of the two factors. Some combinations may result in F/N curves that do not fulfill a predetermined safety target. This article thus proposes an “excess risk index” in order to quantify the road tunnel risk magnitudes that do not pass the safety target. The two‐factor impact analysis can be illustrated by a contour chart based on the excess risk. Finally, the methodology has been applied to Singapore's KPE road tunnel and the results show that in terms of meeting the test safety target for societal risk, the traffic capacity of the tunnel should be no more than 1,200 vehs/h/lane, with a maximum proportion of 18% HGVs.     

Influence of Safety Measures on the Risks of Transporting Dangerous Goods Through Road Tunnels

Quantitative risk assessment (QRA) models are used to estimate the risks of transporting dangerous goods and to assess the merits of introducing alternative risk reduction measures for different transportation scenarios and assumptions. A comprehensive QRA model recently was developed in Europe for application to road tunnels. This model can assess the merits of a limited number of "native safety measures." In this article, we introduce a procedure for extending its scope to include the treatment of a number of important "nonnative safety measures" of interest to tunnel operators and decisionmakers. Nonnative safety measures were not included in the original model specification. The suggested procedure makes use of expert judgment and Monte Carlo simulation methods to model uncertainty in the revised risk estimates. The results of a case study application are presented that involve the risks of transporting a given volume of flammable liquid through a 10-km road tunnel.     

Quantitative Risk Analysis on the Transport of Dangerous Goods Through a Bi‐Directional Road Tunnel

A quantitative risk analysis (QRA) regarding dangerous goods vehicles (DGVs) running through road tunnels was set up. Peak hourly traffic volumes (VHP), percentage of heavy goods vehicles (HGVs), and failure of the emergency ventilation system were investigated in order to assess their impact on the risk level. The risk associated with an alternative route running completely in the open air and passing through a highly populated urban area was also evaluated. The results in terms of social risk, as F/N curves, show an increased risk level with an increase the VHP, the percentage of HGVs, and a failure of the emergency ventilation system. The risk curves of the tunnel investigated were found to lie both above and below those of the alternative route running in the open air depending on the type of dangerous goods transported. In particular, risk was found to be greater in the tunnel for two fire scenarios (no explosion). In contrast, the risk level for the exposed population was found to be greater for the alternative route in three possible accident scenarios associated with explosions and toxic releases. Therefore, one should be wary before stating that for the transport of dangerous products an itinerary running completely in the open air might be used if the latter passes through a populated area. The QRA may help decisionmakers both to implement additional safety measures and to understand whether to allow, forbid, or limit circulation of DGVs.     

The Risk Management of Third Parties During Construction in Multifunctional Urban Locations

Buildings above roads, railways, and existing buildings themselves are examples of multifunctional urban locations. The construction stage of those buildings is in general extremely complicated. Safety is one of the critical issues during the construction stage. Because the traffic on the infrastructure must continue during the construction of the building above the infrastructure, falling objects due to construction activities form a major hazard for third parties, i.e., people present on the infrastructure or beneath it, such as car drivers and passengers. This article outlines a systematic approach to conduct quantitative risk assessment (QRA) and risk management of falling elements for third parties during the construction stage of the building above the infrastructure in multifunctional urban locations. In order to set up a QRA model, quantifiable aspects influencing the risk for third parties were determined. Subsequently, the conditional probabilities of these aspects were estimated by historical data or engineering judgment. This was followed by integrating those conditional probabilities, now used as input parameters for the QRA, into a Bayesian network representing the relation and the conditional dependence between the quantified aspects. The outcome of the Bayesian network—the calculation of both the human and financial risk in quantitative terms—is compared with the risk acceptance criteria as far as possible. Furthermore, the effect of some safety measures were analyzed and optimized in relation with decision making. Finally, the possibility of integration of safety measures in the functional and structural building design above the infrastructure are explored.     

Evaluation of the Consequences of a Cistern Truck Accident While Transporting Dangerous Substances through a Tunnel

The transportation of dangerous substances by truck carriers harbors important safety issues in both road and mine tunnels. Even though traffic conditions in road and mine tunnels are different, the potential geometric and hydrodynamic similarities can lead to similar effects from the uncontrolled leakage of the dangerous material. This work was motivated by the design study of the LAGUNA‐LBNO (Large Apparatus studying Grand Unification and Neutrino Astrophysics and Long Baseline Neutrino Oscillations) project. The considered neutrino detector requires a huge amount of liquid argon, which must be transported down the tunnel. The present work focuses on the estimation of the most credible incident and the resulting consequences in the case of a truck accident in the tunnel. The approach and tools used in the present work are generic and can be adapted to other similar situations.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

Does Concern‐Driven Risk Management Provide a Viable Alternative to QRA?

This article discusses a concept of concern-driven risk management, in which qualitative expert judgments about whether concerns warrant specified risk management interventions are used in preference to quantitative risk assessment (QRA) to guide risk management decisions. Where QRA emphasizes formal quantitative assessment of the probable consequences caused by the recommended actions, and comparison to the probable consequences of alternatives, including the status quo, concern-driven risk management instead emphasizes perceived urgency or severity of the situation motivating recommended interventions. In many instances, especially those involving applications of the precautionary principle, no formal quantification or comparison of probable consequences for alternative decisions is seen as being necessary (or, perhaps, possible or desirable) prior to implementation of risk management measures. Such concern-driven risk management has been recommended by critics of QRA in several areas of applied risk management. Based on case studies and psychological literature on the empirical performance of judgment-based approaches to decision making under risk and uncertainty, we conclude that, although concern-driven risk management has several important potential political and psychological advantages over QRA, it is not clear that it performs better than (or as well as) QRA in identifying risk management interventions that successfully protect human health or achieve other desired consequences. Therefore, those who advocate replacing QRA with concern-driven alternatives, such as expert judgment and consensus decision processes, should assess whether their recommended alternatives truly outperform QRA, by the criterion of producing preferred consequences, before rejecting the QRA paradigm for practical applications.     

Quantitative Risk Assessment of the New York State Operated West Valley Radioactive Waste Disposal Area

This article is based on a quantitative risk assessment (QRA) that was performed on a radioactive waste disposal area within the Western New York Nuclear Service Center in western New York State. The QRA results were instrumental in the decision by the New York State Energy Research and Development Authority to support a strategy of in‐place management of the disposal area for another decade. The QRA methodology adopted for this first of a kind application was a scenario‐based approach in the framework of the triplet definition of risk (scenarios, likelihoods, consequences). The measure of risk is the frequency of occurrence of different levels of radiation dose to humans at prescribed locations. The risk from each scenario is determined by (1) the frequency of disruptive events or natural processes that cause a release of radioactive materials from the disposal area; (2) the physical form, quantity, and radionuclide content of the material that is released during each scenario; (3) distribution, dilution, and deposition of the released materials throughout the environment surrounding the disposal area; and (4) public exposure to the distributed material and the accumulated radiation dose from that exposure. The risks of the individual scenarios are assembled into a representation of the risk from the disposal area. In addition to quantifying the total risk to the public, the analysis ranks the importance of each contributing scenario, which facilitates taking corrective actions and implementing effective risk management. Perhaps most importantly, quantification of the uncertainties is an intrinsic part of the risk results. This approach to safety analysis has demonstrated many advantages of applying QRA principles to assessing the risk of facilities involving hazardous materials.     

交通系统运营管控方案构建

从路网列车流的宏观特性出发,通过引入列车流量和调整措施两类运行参数,在对交通路网进行有效分割的基础上,依据所分割各路段内列车的起始站点和终止站点将其划分为不同队列。针对不同的路网空间分布结构形式,基于列车流调控机制,构建了系统输入变量和状态变量之间的约束关系建立了交通流控制模型。基于特定的调控目标,将交通流的调控过程转化为控制系统的优化设计问题,研究结果可为路网交通流优化控制提供重要参考。     

我国户籍制度下的劳动力转移

本文考虑到现在还在我国普遍实行的农村与城市分离的户口政策,根据流向城市的农村户口劳动力的去向,将城市部门分为两个部门,建立了一个哈利斯—托塔罗模式的劳动力转移模型。并用这个模型分析了农村户口劳动力向城市劳动转移的经济效果。随着经济发展、改革深化,我国现行的户口政策必将发生重大的变化,本文为加快这方面工作的进程,提供了理论依据。     

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

To quantify the on‐road PM_2.5‐related premature mortality at a national scale, previous approaches to estimate concentrations at a 12‐km × 12‐km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on‐road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on‐road PM_2.5‐related premature mortality in central North Carolina with two different concentration estimation approaches—(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36‐km × 36‐km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space–time interpolation technique to provide annual average PM_2.5 concentrations at a Census‐block level (～105,000 Census blocks). The hybrid modeling approach estimated 24% more on‐road PM_2.5‐related premature mortality than CMAQ. The major difference is from the primary on‐road PM_2.5 where the hybrid approach estimated 2.5 times more primary on‐road PM_2.5‐related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on‐road PM_2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near‐road primary PM_2.5 and suggesting that previous studies may have underestimated premature mortality due to PM_2.5 from traffic‐related emissions.     

Assessing Potential Human Health Hazards and Benefits from Subtherapeutic Antibiotics in the United States: Tetracyclines as a Case Study

Many scientists, activists, regulators, and politicians have expressed urgent concern that using antibiotics in food animals selects for resistant strains of bacteria that harm human health and bring nearer a “postantibiotic era” of multidrug resistant “super‐bugs.” Proposed political solutions, such as the Preservation of Antibiotics for Medical Treatment Act (PAMTA), would ban entire classes of subtherapeutic antibiotics (STAs) now used for disease prevention and growth promotion in food animals. The proposed bans are not driven by formal quantitative risk assessment (QRA), but by a perceived need for immediate action to prevent potential catastrophe. Similar fears led to STA phase‐outs in Europe a decade ago. However, QRA and empirical data indicate that continued use of STAs in the United States has not harmed human health, and bans in Europe have not helped human health. The fears motivating PAMTA contrast with QRA estimates of vanishingly small risks. As a case study, examining specific tetracycline uses and resistance patterns suggests that there is no significant human health hazard from continued use of tetracycline in food animals. Simple hypothetical calculations suggest an unobservably small risk (between 0 and 1.75E‐11 excess lifetime risk of a tetracycline‐resistant infection), based on the long history of tetracycline use in the United States without resistance‐related treatment failures. QRAs for other STA uses in food animals also find that human health risks are vanishingly small. Whether such QRA calculations will guide risk management policy for animal antibiotics in the United States remains to be seen.     

Hypothetical Fears and Quantitative Risk Analysis

Hypothetical fears are concepts, not quantities. Their conceptual nature makes impractical conventional quantitative risk analyses (QRA) based on benefit/cost/risk, so they become an unmeasured influence in national decision making. The decision process involves two steps, the Analytic Stage (QRA based) and the Priority Stage (resource allocation competition). This article suggests that a quantitative estimate of the social cost of fear reduction to acceptable levels be used as a surrogate QRA input to the Priority Stage.     

Bayesian Methodology for Model Uncertainty Using Model Performance Data

A simple and useful characterization of many predictive models is in terms of model structure and model parameters. Accordingly, uncertainties in model predictions arise from uncertainties in the values assumed by the model parameters (parameter uncertainty) and the uncertainties and errors associated with the structure of the model (model uncertainty). When assessing uncertainty one is interested in identifying, at some level of confidence, the range of possible and then probable values of the unknown of interest. All sources of uncertainty and variability need to be considered. Although parameter uncertainty assessment has been extensively discussed in the literature, model uncertainty is a relatively new topic of discussion by the scientific community, despite being often the major contributor to the overall uncertainty. This article describes a Bayesian methodology for the assessment of model uncertainties, where models are treated as sources of information on the unknown of interest. The general framework is then specialized for the case where models provide point estimates about a single‐valued unknown, and where information about models are available in form of homogeneous and nonhomogeneous performance data (pairs of experimental observations and model predictions). Several example applications for physical models used in fire risk analysis are also provided.     

Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model

Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway‐Maxwell Poisson (COM‐Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM‐Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM‐Poisson GLM, and (2) estimate the prediction accuracy of the COM‐Poisson GLM using simulated data sets. The results of the study indicate that the COM‐Poisson GLM is flexible enough to model under‐, equi‐, and overdispersed data sets with different sample mean values. The results also show that the COM‐Poisson GLM yields accurate parameter estimates. The COM‐Poisson GLM provides a promising and flexible approach for performing count data regression.     

How Useful Is Quantitative Risk Assessment?

This article discusses the use of quantitative risk assessment (QRA) in decision making regarding the safety of complex technological systems. The insights gained by QRA are compared with those from traditional safety methods and it is argued that the two approaches complement each other. It is argued that peer review is an essential part of the QRA process. The importance of risk-informed rather than risk-based decision making is emphasized. Engineering insights derived from QRAs are always used in combination with traditional safety requirements and it is in this context that they should be reviewed and critiqued. Examples from applications in nuclear power, space systems, and an incinerator of chemical agents are given to demonstrate the practical benefits of QRA. Finally, several common criticisms raised against QRA are addressed.     

Dread and Risk Elimination Premium for the Value of a Statistical Life

The value of a statistical life (VSL) is a widely used measure for the value of mortality risk reduction. As VSL should reflect preferences and attitudes to risk, there are reasons to believe that it varies depending on the type of risk involved. It has been argued that cancer should be considered a “dread disease,” which supports the use of a “cancer premium.” The objective of this study is to investigate the existence of a cancer premium (for pancreatic cancer and multiple myeloma) in relation to road traffic accidents, sudden cardiac arrest, and amyotrophic lateral sclerosis (ALS). Data were collected from 500 individuals in the Swedish general population of 50–74‐year olds using a web‐based questionnaire. Preferences were elicited using the contingent valuation method, and a split‐sample design was applied to test scale sensitivity. VSL differs significantly between contexts, being highest for ALS and lowest for road traffic accidents. A premium (92–113%) for cancer was found in relation to road traffic accidents. The premium was higher for cancer with a shorter time from diagnosis to death. A premium was also found for sudden cardiac arrest (73%) and ALS (118%) in relation to road traffic accidents. Eliminating risk was associated with a premium of around 20%. This study provides additional evidence that there exist a dread premium and risk elimination premium. These factors should be considered when searching for an appropriate value for economic evaluation and health technology assessment.     

基于韧性城市视角的冰雪天气下路网恢复问题研究

城市的快速发展使其愈加依赖于生命线基础设施系统,城市在自然或人为突发事件面前的脆弱性日益凸显,城市面对突发事件后的运行与恢复问题受到广泛关注。冬季极端冰雪天气对城市路网系统带来极大冲击,严重降低路网服务能力。本文基于韧性城市视角,对冰雪天气下城市路网韧性的概念和度量方法进行了分析。以提升路网韧性为目标,建立冰雪天气下路网恢复问题的数学模型,解决极端冰雪天气不确定信息下的城市路网除雪应急物资布局问题及其除雪作业优化问题,并设计了相应的启发式求解算法。最后通过算例验证了模型和算法的有效性,以期为城市冰雪天气应对提供决策支持,提升城市应对极端冰雪天气的韧性。     

How Safe Is Safe Enough for Self‐Driving Vehicles?

Self‐driving vehicles (SDVs) promise to considerably reduce traffic crashes. One pressing concern facing the public, automakers, and governments is “How safe is safe enough for SDVs?” To answer this question, a new expressed‐preference approach was proposed for the first time to determine the socially acceptable risk of SDVs. In our between‐subject survey (N = 499), we determined the respondents’ risk‐acceptance rate of scenarios with varying traffic‐risk frequencies to examine the logarithmic relationships between the traffic‐risk frequency and risk‐acceptance rate. Logarithmic regression models of SDVs were compared to those of human‐driven vehicles (HDVs); the results showed that SDVs were required to be safer than HDVs. Given the same traffic‐risk‐acceptance rates for SDVs and HDVs, their associated acceptable risk frequencies of SDVs and HDVs were predicted and compared. Two risk‐acceptance criteria emerged: the tolerable risk criterion, which indicates that SDVs should be four to five times as safe as HDVs, and the broadly acceptable risk criterion, which suggests that half of the respondents hoped that the traffic risk of SDVs would be two orders of magnitude lower than the current estimated traffic risk. The approach and these results could provide insights for government regulatory authorities for establishing clear safety requirements for SDVs.